EpiMax

Blood and Aging

We have all heard those particularly haunting tales about witches remaining ever youthful by imbibing a young woman?s blood, but until a few years ago these tales were only told to frighten children before bed. Last year, SAGE reported on a study where the blood of a young mouse was sufficient to rejuvenate an older mouse. This study lent credence to the idea that there must be something substantially different in young blood compared to old.

To examine the changes that occur in blood as an individual ages, Dr. Andrew Johnson?s lab, at NIH/NHLBI (National Institute of Health/National Heart, Lung and Blood Institute) conducted an extensive study using thousands of patient blood samples, the study was then replicated, further verifying the results.

The researchers chose to analyze the blood samples transcriptome, a measurement of the RNA transcripts from each gene. The compilation of RNA transcripts is a reflection of the relative expression levels of the genome at a given point in time. The choice to examine the transcriptome was pivotal, as all the cells in an organism will have the same DNA and this DNA does not generally change during the person?s lifetime, thus making DNA genomic analysis less useful for an age-related study. What does change over a person?s lifetime is modifications of DNA, which genes are expressed from the DNA and the relative levels of expression of each gene.

The study, which has been published in Nature Communications, used certain types of blood cells and brain tissue to examine the age-associated changes in gene expression. In a remarkable show of replication, the study was initially performed with blood samples from individuals of European ancestry and then replicated in additional European ancestry samples, totaling an amazing 14,983 individual European ancestry samples. The study was then extended to various ethnic groups, including samples from individuals of Hispanic, African, or Native American ancestry. The study identified 1,497 genes in blood cells and/or brain tissue that showed significantly differential expression patterns in older individuals when compared to younger individuals.

The expression of the gene can either be negatively correlated (expressed at a lower level) or positively correlated (expressed at a higher level) in relation to chronological age. There were three distinct groups of genes that were negatively correlated with chronological age. The first group included three subgroups: ribosomal genes (factories on which a RNA is translated into a protein), mitochondrial genes (energy factories of the cells), and genes associated with DNA replication and repair (DNA maintenance and fidelity). All of the genes associated with these subgroups are vitally important to the health of a cell and tissue. The second large group consisted of genes associated with immunity. The third large group was composed of genes that code for the actual ribosomal subunits. Decreased gene expression could help explain the decreased ?health? of older cells and increased mutation rates in older cells. There were also four groups of genes positively correlated with age, which were focused on cellular structure, immunity, fatty acid metabolism, and lysosome activity. Several of the genes in these clusters had been previously identified in other age-related screens in various model organisms, further supporting this study?s methods and findings.

Another interesting finding in this study involved epigenetic patterns, specifically methylation on cytosines (one of the four nucleotide bases in DNA) and the predictive. Epigenetics can be thought of as the ?grammar? of DNA, as it doesn?t change the underlying pattern of DNA base pairs, but rather instructs how a gene is to be expressed. Methylation on cytosines is an epigenetic mark that can have a regulatory effect on how or if a gene is expressed. Methylation patterns are also dynamic, meaning that this pattern can change over time. This study showed that those genes whose expression pattern changed with age were highly enriched for the presence of regulatory cytosines. This could indicate how gene expression is controlled as the individual ages. There are several targeted methylation therapies in development that might potentially offer the ability to effectively and safely alter these methylation patterns for therapeutic purposes. The authors found that by combining the transcriptomic expression patterns and the epigenetic patterns a ?chronological? age predictor could be used to better understand an individual?s ?age? in terms of health. Further refinement is needed, but this type of predictor could have a substantial impact on prediction, diagnosis and treatment of individuals, perhaps even allowing for preventive treatments before symptoms progress to disease level changes.

The sheer magnitude of this study, from the number of samples to the ethnic diversity of the participants, makes it a pioneer in the rapidly expanding field of transcriptomics. Until a few years ago, the methods and budgets did not exist for such a study to take place, but as the technology continues to increase, costs decline and more data will become available, enhancing our understanding of aging and allowing for us to better cope with age-associated changes. The 1,497 genes identified as being associated with chronological age offer a plethora of new targets from which we can better understand the aging process and age-related diseases. With the current progress being made in the gene therapy and drug fields it is possible that some of these 1,497 genes could potentially be manipulated to ameliorate many age-related diseases.